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Online Unsupervised Multi-view Feature Selection
, 2016 In the era of big data, it is becoming common to have data with multiple modalities or coming from multiple sources, known as "multi-view data". Multi-view data are usually unlabeled and come from high-dimensional spaces (such as language vocabularies ...
He, Lifang +4 more
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Unsupervised feature selection method for intrusion detection system [PDF]
, 2015 © 2015 IEEE. This paper considers the feature selection problem for data classification in the absence of data labels. It first proposes an unsupervised feature selection algorithm, which is an enhancement over the Laplacian score method, named an ...
Ambusaidi, MA, He, X, Nanda, P
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Efficient Feature Ranking and Selection Using Statistical Moments
IEEE AccessUnsupervised feature selection methods can be more efficient than supervised methods, which rely on the expensive and time-consuming data labeling process.
Yael Hochma, Yuval Felendler, Mark Last
doaj +1 more source
Cluster Density Properties Define a Graph for Effective Pattern Feature Selection
IEEE Access, 2020 Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without ...
Khadidja Henni +2 more
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Labeling the Features Not the Samples: Efficient Video Classification with Minimal Supervision
, 2015 Feature selection is essential for effective visual recognition. We propose an efficient joint classifier learning and feature selection method that discovers sparse, compact representations of input features from a vast sea of candidates, with an almost
Baluja, Shumeet +3 more
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BMC Medical Genomics, 2022 Background Feature selection of multi-omics data analysis remains challenging owing to the size of omics datasets, comprising approximately $$10^2$$ 10 2 – $$10^5$$ 10 5 features. In particular, appropriate methods to weight individual omics datasets are
Y-h. Taguchi, Turki Turki
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Unsupervised Feature Selection and Clustering Optimization Based on Improved Differential Evolution
IEEE Access, 2019 The feature selection method based on supervised learning has been widely studied and applied to the field of machine learning and data mining. But unsupervised feature selection is still a tricky area of research because the unavailability of the label ...
Tao Li, Hongbin Dong
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Performance analysis of unsupervised feature selection methods
, 2012 Feature selection (FS) is a process which attempts to select more informative features. In some cases, too many redundant or irrelevant features may overpower main features for classification.
Inbarani, H. Hannah +2 more
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UNLABELED SELECTED SAMPLES IN FEATURE EXTRACTION FOR CLASSIFICATION OF HYPERSPECTRAL IMAGES WITH LIMITED TRAINING SAMPLES [PDF]
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 2015 Feature extraction plays a key role in hyperspectral images classification. Using unlabeled samples, often unlimitedly available, unsupervised and semisupervised feature extraction methods show better performance when limited number of training samples ...
A. Kianisarkaleh +2 more
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Molecular Oncology, EarlyView.Adaptaquin selectively kills glioma stem cells while sparing differentiated brain cells. Transcriptomic and proteomic analyses show Adaptaquin disrupts iron and cholesterol homeostasis, with iron chelation amplifying cytotoxicity via cholesterol depletion, mitochondrial dysfunction, and elevated reactive oxygen species.
Adrien M. Vaquié +16 more
wiley +1 more source